This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species’ geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes. We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype-environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RA..., Raw sequence data is available at the European Nucleotide Archive (ENA): Myotis escalerai and M. crypticus (PRJEB29086), and the NCBI Short Read Archive (SRA): Afrixalus fornasini – (SRP150605). Input data (processed genomic data and spatial-environmental data prior to running the toolbox) available as part of this repository. Methods: see methods text of manuscript and tutorials: Setup and running the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette, Full tutorials for setup and running the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette This software is intended for HPC use. Please make sure the software below is installed and functional in your HPC environment before proceeding:

Life on the edge data and scripts (also available here: https://github.com/cd-barratt/Life_on_the_edge)

Singularity (3.5) and bioconductor container with correct R version:Â https://cloud.sylabs.io/library/sinwood/bioconductor/bioconductor_3.14

R (4.1.3). Dependencies for toolbox installed within R version in singularity container upon setup (you specify your R libraries in the script where annotated) Julia (1.7.2)

Additionally you need to download the following and place in the correct directories to be sure the toolbox will function properly: * Environmental predictor data - please download and place environmental layers used for SDMs, GEAs etc in separate folders for current and future environmental conditions. These f..., # Life on the edge: a new toolbox for population-level climate change vulnerability assessments

Dataset contains input files needed to run Life on the edge for an example dataset (Afrixalus fornasini) You may run data for your focal species following the structure and content of the example files provided

First you need to download the following and place in the correct directories to be sure the toolbox will function properly:

• Environmental predictor data (e.g. Worldclim2/CHELSA, land cover, see below)
• A working plink and maxent version (see below)
• Country border data (e.g. Natural Earth data, see below)

Tutorials for initial setup and running the toolbox

Full setup and how to run the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette

Description of the data and file structure

• Params.tsv is a tab separated file that contains all parameters for running each species ...

The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors.

Abstract: "Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models."

Observations

The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019.

Slocum glider data
The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals.

Relevant data name: slocum_grid_processed.nc

Slocum glider Microstructure data:
The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995).

Relevant data name: slocum_eps_ds_processed_era5_23Sep2022.nc
Relevant data name: mixing_layer_xld.csv

Slocum glider SST data: Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive.

Relevant data name: slocum_sst_mean_10m_20231110..nc

Wave Glider data
The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data provided by ECMWF available at https://doi.org/10.24381/cds.bd0915c6.

Relevant data name: WG_era5_1h_processed_28Aug2022.nc

Storm tracking dataset
To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication:

Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2022JC018925

Data can be accessed at https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker

All Southern Ocean storm locations can be found at: ec_centers_1981_2020.nc

EN4 mixed layer depths
We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 2 x 2 degree grid cells, then obtain a mean value for each DJF season per 2 x 2 degree grid cell.

Relevant data name: en4_monthly_mixed_layer_depth_median.nc

Southern Ocean Fronts
Position of the Subantarctic Front and Polar Front are from: